Fingerprint recognition is a growing market hence the need for an ongoing development of increasing the performance as well as reducing the computational complexity in order to achieve efficient match algorithms. By using the minutia points as a characterization of a fingerprint and follow the current standards in how minutia points are defined, e.g. International STANDARD ISO/IEC 19794-2 Information Technology, Biometric data interchange formats Part 2: Finger Minutiae data, (2005), fingerprint matching technology has taken a further step towards mass-market solutions.
Matching two sets of minutia points straight ahead, point by point, is a very difficult task since there is almost always some kind of rotation and/or translation between the sets. This is because the placement of the finger on the sensor is usually not on the exact same position as it might have altered translation or rotation wise. I order to overcome this problem, some kind of alignment procedure could be performed where e.g. a verification set is de-translated and/or de-rotated to be in a similar position as an enrollment set. Once the two sets of minutia points are aligned, the individual minutia points could for example be matched all against all to decide which minutia points match between the two sets. Finally a metric could be calculated based on how many and how well the minutia points actually match. The pre-processing of the data, i.e. de-rotating and de-translating, adds requirements on computational effort. Simultaneously, user demands put swiftness high ranked, which is counteracted by the additional processing.
Isenor et al, “Fingerprint matching using graph matching”, University of Toronto, discloses an approach where a fingerprint is encoded in form of a graph, in which nodes represent ridges and edges represent ridge adjacency information. A directed graph is thus defined, where each node in the graph corresponds to one ridge. Edges between nodes are labeled based on the nodes they interconnect. The graphs are partitioned, refined and thereafter scored by tracing a tree in each graph.
WO 96/12246 A1 discloses an image comparison arrangement where fingerprint minutia maps are converted to attributed relation graphs, ARG, including nodes and branches. In the ARG, a comparison tree by which attempts are made to fill a match core with elements representing matching stars. The number of elementst in the match core indicates the degree of match.
EP 2237226 A1 discloses an approach for inter-pattern feature correspondence relationships. A proximity feature point group is generated in which feature points are positionally proximate to each other in a pattern and a location relationship number numeric group indicating location relationship between the feature points. The feature point related arrangement relationships numerical value used for matching check of the feature point group arrangements is measured by using a general image coordinate system for a reference coordinate system. A score is derived from the number of corresponding feature points.
GB 2050026 A discloses an approach where minutiae of a fingerprint are combined with counts and density for each feature point selected as a reference feature point. The density is determined in connection with adjacent feature points that are present in a predetermined neighbourhood of the reference feature point. Each count is decided by number of streaks intervening between the reference feature point among the feature points in a predetermined sector of the neighbourhood.
The solutions presented above still are complex and/or resource consuming. It is therefore a desire to provide an improved approach of biometric matching of sets of minutiae.